In general, throughout the life cycle of an electrochemical device, its performance tends to gradually deteriorate. This can come from irreversible physicochemical changes that take place mainly during the operation of the device, but also when the device is stopped.
It can then be essential to have information on the state of general deterioration of the device, to thereby know how far it has advanced its life cycle.
Moreover, during operation of an electrochemical device, certain physical phenomena can take place that cause a sudden drop in performance. For example, in the case of fuel cells of the proton exchange membrane type, the engorgement and drainage phenomena cause substantial deterioration of the cell's performance.
It is therefore desirable to be able to detect these phenomena in real time to resolve them rapidly.
Determining the overall deterioration of the state of the device, as well as detecting certain harmful phenomena, amounts to determining a state of health of the electrochemical device.
The state of health of the electrochemical device can be defined as a deviation between the estimated or measured value of at least one physicochemical parameter at least partially modeling the physicochemical behavior of the electrochemical device, and a predetermined reference value of the same parameter. This deviation translates the operational nature of the device both in the long term (remaining lifetime) and short term (appearance of physical phenomena that abruptly decrease the performance). Of course, it may involve a relative or absolute deviation.
Of course, the notion of state of health covers various realities, which depend on information that the user wishes to obtain.
In general, a state of health may be determined from a set of characteristic parameters providing information on the static and/or dynamic behavior of said electrochemical device. These parameters can represent the different physicochemical phenomena coming into play within the electrochemical device, such as, for example, phenomena related to the chemical kinetics, ohmic phenomena, and, in the case of a fuel cell, diffusion phenomena of reactive fluids to the reaction sites.
As an example, determining a state of health of a fuel cell of the proton exchange membrane (PEM) type can comprise a real-time estimate of certain parameters such as the internal electrical resistance (generally likened to the electrical resistance of the membrane) of the cell, the moisture content of the membrane, or parameters relative to the diffusion phenomena or phenomena related to the chemical kinetics, with the aim of monitoring its state of general deterioration on the one hand, and detecting a drainage phenomenon of the membrane on the other hand. Thus, it is possible to react more quickly to prevent this type of phenomenon from lasting and irreversibly damaging the cell.
Several parameters estimating methods, for determining a state of health of the device, are commonly used in the context of PEM-type fuel cells.
In general, each of the methods usually comprises a first step of obtaining experimental data and a second step for exploiting measurements to estimate one or more characteristic parameters.
The first step consists of applying a particular electric excitation to the electrochemical device and measuring the electrical response thereof.
The second step is the analysis of the input and output signals to estimate one or more parameters at least partially characterizing the behavior of the cell. These parameters can belong to a static and/or dynamic model representing the global or partial behavior of the cell.
Lastly, a state of health of the cell is estimated as the deviation between the estimated value of the parameter(s) with a predetermined reference value with the same parameter(s).
A first method is based on the known electrochemical impedance spectroscopy technique. It can be used to detect the engorgement or drainage of a fuel cell, as described in the article by Fouquet et al. entitled “Model based PEM fuel cell state-of-health monitoring via ac impedance measurements” and published in 2006 in the Journal of Power Sources, 159, 905-913.
An input current is applied to the cell, which has a sequence of low-amplitude sinusoidal perturbations scanning a large range of frequencies. The voltage in response to these perturbations is measured at the terminals of the cell. It is then possible to obtain the impedance of the cell using an impedance analysis device. This impedance can be traced in the Nyquist plane, which gives the evolution of the imaginary part of the impedance as a function of its real part.
Then, the value of parameters of a model of said impedance (small signal dynamic model), of the equivalent electrical cell type, is estimated from signals measured using an optimization method, for example the method of least squares. The impedance model can be the following equivalent electrical circuit:
where Rmem, Rdiff, Ract are respectively the resistances of the membrane, for diffusion and activation, and Cdc and Cdiff are respectively the double layer capacity and the capacity of the equivalent diffusion layer. A description of the use of this type of model can be found in the thesis by Fontès entitled “Modélisation et caractérisation de la pile PEM pour l'étude des interactions avec les convertisseurs statiques,” 2005, Institut National Polytechnique de Toulouse, in section 3.4.3 “Exploitation of impedance diagrams.”
The estimated value of one or more parameters of the model is then compared to a predetermined reference value of the same parameter(s). The deviation between the estimated value and the reference value characterizes the state of health of the cell.
A second estimating method, also known by those skilled in the art, is based on the study of the voltage response of the electrochemical device upon application of a current step such as, for example, a current interruption.
This method is in particular described in the article by Cho et al. entitled “Transient response of a unit proton-exchange membrane fuel cell under various operating conditions” and published in 2008 in the Journal of Power Sources 185, 118-128.
From measurements of the applied current and the voltage response, it is possible to deduce the internal electrical resistance of the device simply.
One then calculates a deviation by comparing the value deduced from the internal electrical resistance at a predetermined reference value, said deviation characterizing the state of health of the cell.
A third method, called high-amplitude scanning, is described in the aforementioned thesis by Fontès.
It consists of applying, to the electrochemical device, a periodic input current, preferably low frequency, and an amplitude corresponding to a current range going substantially from zero to the nominal current, and measuring the response voltage. Inasmuch as the scanning frequency is very low, the current/voltage polarization curve is drawn automatically.
Then, the parameters of a static or dynamic model, depending on the frequency of the scanning, of the electrochemical device describing the evolution of the voltage as a function of the applied current are estimated using experimental values, for example using an optimization method of the least squares type.
As an illustration, as shown by the Fontès 2005 thesis in section 3.4.1 entitled “Static exploitation of voltage-current curves” regarding a PEM-type fuel cell, the experimental polarization curve Ucellule=f(I), at a constant temperature and pressure, can be compared to the following static four-variable model:
      U    cellule    =            E      th        -                            R          ·          T                                      α            *                    ·          n          ·          F                    ⁢              ln        ⁡                  (                      I                          I              0              *                                )                      -                  (                              R                          diff              ,              0                                +                      R            mem                          )            ·      I      where α* and I*0 are parameters relative to the activation over-voltages, Rmem is the electrical resistance of the cell due primarily to the ohmic losses in the membrane, and Rdiff, 0 is a resistance related to losses by diffusion, and in particular to diffusion over-voltages. As indicated above, the different parameters of the model can be estimated using an optimization method of the least squares type.
As before, one then calculates a deviation by comparing the estimated value of one or more parameters of the static model to a predetermined reference value of the same parameter(s), said deviation characterizing the state of health of the cell.
However, the operation time and/or quality of the results of each of these different methods can prove unsatisfactory. The performance time of a method designates, here and in the rest of the document, the time to obtain experimental data and process it to obtain the parameters. The quality of the results, here and in the rest of the document, designates, on the one hand, the coherence of the parameters obtained with the known and expected orders of magnitude, and on the other hand, the precision in terms of possible intervals of values for each of these parameters (ideally this interval is reduced to a single value for each parameter).
Furthermore, each method can only provide limited information in terms of accessible parameters, which can be insufficient to determine an actual state of health of the studied electrochemical device. One solution could consist of applying these different methods successively, but the processing time would be prohibitive, without, furthermore, improving the robustness of each method.